{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<small><i>This notebook was put together by [Jake Vanderplas](http://www.vanderplas.com). Source and license info is on [GitHub](https://github.com/jakevdp/sklearn_tutorial/).</i></small>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Scikit-Learn: Machine Learning with Python\n",
    "\n",
    "This session will cover the basics of Scikit-Learn, a popular package containing a collection of tools for machine learning written in Python. See more at http://scikit-learn.org."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outline\n",
    "\n",
    "**Main Goal:** To introduce the central concepts of machine learning, and how they can be applied in Python using the Scikit-learn Package.\n",
    "\n",
    "- Definition of machine learning\n",
    "- Data representation in scikit-learn\n",
    "- Introduction to the Scikit-learn API"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## About Scikit-Learn\n",
    "\n",
    "[Scikit-Learn](http://github.com/scikit-learn/scikit-learn) is a Python package designed to give access to **well-known** machine learning algorithms within Python code, through a **clean, well-thought-out API**. It has been built by hundreds of contributors from around the world, and is used across industry and academia.\n",
    "\n",
    "Scikit-Learn is built upon Python's [NumPy (Numerical Python)](http://numpy.org) and [SciPy (Scientific Python)](http://scipy.org) libraries, which enable efficient in-core numerical and scientific computation within Python. As such, scikit-learn is not specifically designed for extremely large datasets, though there is [some work](https://github.com/ogrisel/parallel_ml_tutorial) in this area.\n",
    "\n",
    "For this short introduction, I'm going to stick to questions of in-core processing of small to medium datasets with Scikit-learn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is Machine Learning?\n",
    "\n",
    "In this section we will begin to explore the basic principles of machine learning.\n",
    "Machine Learning is about building programs with **tunable parameters** (typically an\n",
    "array of floating point values) that are adjusted automatically so as to improve\n",
    "their behavior by **adapting to previously seen data.**\n",
    "\n",
    "Machine Learning can be considered a subfield of **Artificial Intelligence** since those\n",
    "algorithms can be seen as building blocks to make computers learn to behave more\n",
    "intelligently by somehow **generalizing** rather that just storing and retrieving data items\n",
    "like a database system would do.\n",
    "\n",
    "We'll take a look at two very simple machine learning tasks here.\n",
    "The first is a **classification** task: the figure shows a\n",
    "collection of two-dimensional data, colored according to two different class\n",
    "labels. A classification algorithm may be used to draw a dividing boundary\n",
    "between the two clusters of points:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "# set seaborn plot defaults.\n",
    "# This can be safely commented out\n",
    "import seaborn; seaborn.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jakevdp/anaconda/envs/python3.4/lib/python3.4/site-packages/matplotlib/collections.py:650: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors_original != str('face'):\n",
      "/Users/jakevdp/anaconda/envs/python3.4/lib/python3.4/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == str('face'):\n"
     ]
    },
    {
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z6lFRUY6//vUJLFq0RLB+aWkpsrOzodVqkZLS5+pp3FCkpdm+Uej++x/C/fc/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\nntM6kK4AAAABSURBVAVYUolnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10457c438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import the example plot from the figures directory\n",
    "from fig_code import plot_sgd_separator\n",
    "plot_sgd_separator()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This may seem like a trivial task, but it is a simple version of a very important concept.\n",
    "By drawing this separating line, we have learned a model which can **generalize** to new\n",
    "data: if you were to drop another point onto the plane which is unlabeled, this algorithm\n",
    "could now **predict** whether it's a blue or a red point.\n",
    "\n",
    "If you'd like to see the source code used to generate this, you can either open the\n",
    "code in the `figures` directory, or you can load the code using the `%load` magic command:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next simple task we'll look at is a **regression** task: a simple best-fit line\n",
    "to a set of data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jakevdp/anaconda/envs/python3.4/lib/python3.4/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == str('face'):\n"
     ]
    },
    {
     "data": {
      "image/png": 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HfpbnNi4g8toM7OXLl6u8vFwzZ87U/fffr4SEBCPGBSCCKqrrtG5bqf7ezh3g7cFtXEBk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\nGXMRLNzzcaXwNzS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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1067d4a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from fig_code import plot_linear_regression\n",
    "plot_linear_regression()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, this is an example of fitting a model to data, such that the model can make\n",
    "generalizations about new data.  The model has been **learned** from the training\n",
    "data, and can be used to predict the result of test data:\n",
    "here, we might be given an x-value, and the model would\n",
    "allow us to predict the y value.  Again, this might seem like a trivial problem,\n",
    "but it is a basic example of a type of operation that is fundamental to\n",
    "machine learning tasks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Representation of Data in Scikit-learn\n",
    "\n",
    "Machine learning is about creating models from data: for that reason, we'll start by\n",
    "discussing how data can be represented in order to be understood by the computer.  Along\n",
    "with this, we'll build on our matplotlib examples from the previous section and show some\n",
    "examples of how to visualize data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most machine learning algorithms implemented in scikit-learn expect data to be stored in a\n",
    "**two-dimensional array or matrix**.  The arrays can be\n",
    "either ``numpy`` arrays, or in some cases ``scipy.sparse`` matrices.\n",
    "The size of the array is expected to be `[n_samples, n_features]`\n",
    "\n",
    "- **n_samples:**   The number of samples: each sample is an item to process (e.g. classify).\n",
    "  A sample can be a document, a picture, a sound, a video, an astronomical object,\n",
    "  a row in database or CSV file,\n",
    "  or whatever you can describe with a fixed set of quantitative traits.\n",
    "- **n_features:**  The number of features or distinct traits that can be used to describe each\n",
    "  item in a quantitative manner.  Features are generally real-valued, but may be boolean or\n",
    "  discrete-valued in some cases.\n",
    "\n",
    "The number of features must be fixed in advance. However it can be very high dimensional\n",
    "(e.g. millions of features) with most of them being zeros for a given sample. This is a case\n",
    "where `scipy.sparse` matrices can be useful, in that they are\n",
    "much more memory-efficient than numpy arrays."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Data Layout](images/data-layout.png)\n",
    "\n",
    "(Figure from the [Python Data Science Handbook](https://github.com/jakevdp/PythonDataScienceHandbook))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A Simple Example: the Iris Dataset\n",
    "\n",
    "As an example of a simple dataset, we're going to take a look at the\n",
    "iris data stored by scikit-learn.\n",
    "The data consists of measurements of three different species of irises.\n",
    "There are three species of iris in the dataset, which we can picture here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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T3Mjj93NOUgsM9KrBuaeCSa+sR0s3zpAuFQ25wNucnvVSfRrqHJKhh7Vt2lt5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\nR+NS3ibZDjvXJWjaVwZScYNRMasSDjNQMKhEn//Z\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iris Setosa\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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HB+4waiiwuCwBIJUsfQdQfp/Sjsx2tGlAe5ooJxtLq3KKPPjz35FAfFd1zMFI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\n9a9XqXUjhf/Z\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iris Versicolor\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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mg6Z4s0pPD2uskFoH/wBC1Zk3S6c2OAD1MecZXI+vFfM/iPwTrHhOSbTNQ0Qw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naQdh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\nEzRnY6lWLDA/WqdzfXEc0Vtp9oUWNtkcRGWZvU47V9ArWOuHu6n/2Q==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iris Virginica\n"
     ]
    }
   ],
   "source": [
    "from IPython.core.display import Image, display\n",
    "display(Image(filename='images/iris_setosa.jpg'))\n",
    "print(\"Iris Setosa\\n\")\n",
    "\n",
    "display(Image(filename='images/iris_versicolor.jpg'))\n",
    "print(\"Iris Versicolor\\n\")\n",
    "\n",
    "display(Image(filename='images/iris_virginica.jpg'))\n",
    "print(\"Iris Virginica\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quick Question:\n",
    "\n",
    "**If we want to design an algorithm to recognize iris species, what might the data be?**\n",
    "\n",
    "Remember: we need a 2D array of size `[n_samples x n_features]`.\n",
    "\n",
    "- What would the `n_samples` refer to?\n",
    "\n",
    "- What might the `n_features` refer to?\n",
    "\n",
    "Remember that there must be a **fixed** number of features for each sample, and feature\n",
    "number ``i`` must be a similar kind of quantity for each sample."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the Iris Data with Scikit-Learn\n",
    "\n",
    "Scikit-learn has a very straightforward set of data on these iris species.  The data consist of\n",
    "the following:\n",
    "\n",
    "- Features in the Iris dataset:\n",
    "\n",
    "  1. sepal length in cm\n",
    "  2. sepal width in cm\n",
    "  3. petal length in cm\n",
    "  4. petal width in cm\n",
    "\n",
    "- Target classes to predict:\n",
    "\n",
    "  1. Iris Setosa\n",
    "  2. Iris Versicolour\n",
    "  3. Iris Virginica\n",
    "  \n",
    "``scikit-learn`` embeds a copy of the iris CSV file along with a helper function to load it into numpy arrays:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target_names', 'feature_names', 'DESCR', 'target'])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 4)\n",
      "[ 5.1  3.5  1.4  0.2]\n"
     ]
    }
   ],
   "source": [
    "n_samples, n_features = iris.data.shape\n",
    "print((n_samples, n_features))\n",
    "print(iris.data[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 4)\n",
      "(150,)\n"
     ]
    }
   ],
   "source": [
    "print(iris.data.shape)\n",
    "print(iris.target.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2]\n"
     ]
    }
   ],
   "source": [
    "print(iris.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['setosa' 'versicolor' 'virginica']\n"
     ]
    }
   ],
   "source": [
    "print(iris.target_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This data is four dimensional, but we can visualize two of the dimensions\n",
    "at a time using a simple scatter-plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jakevdp/anaconda/envs/python3.4/lib/python3.4/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == str('face'):\n"
     ]
    },
    {
     "data": {
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sa4+o4h4l57jvYw81aYpl6Rx+eGUM6LS89esSypQpW9KxCqzjgFD2VijP2V17CKpdg9bd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\ncIuIiFiICreIiIiF/B8KMCIrsG69twAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a8d9240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x_index = 0\n",
    "y_index = 1\n",
    "\n",
    "# this formatter will label the colorbar with the correct target names\n",
    "formatter = plt.FuncFormatter(lambda i, *args: iris.target_names[int(i)])\n",
    "\n",
    "plt.scatter(iris.data[:, x_index], iris.data[:, y_index],\n",
    "            c=iris.target, cmap=plt.cm.get_cmap('RdYlBu', 3))\n",
    "plt.colorbar(ticks=[0, 1, 2], format=formatter)\n",
    "plt.clim(-0.5, 2.5)\n",
    "plt.xlabel(iris.feature_names[x_index])\n",
    "plt.ylabel(iris.feature_names[y_index]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quick Exercise:\n",
    "\n",
    "**Change** `x_index` **and** `y_index` **in the above script\n",
    "and find a combination of two parameters\n",
    "which maximally separate the three classes.**\n",
    "\n",
    "This exercise is a preview of **dimensionality reduction**, which we'll see later."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other Available Data\n",
    "They come in three flavors:\n",
    "\n",
    "- **Packaged Data:** these small datasets are packaged with the scikit-learn installation,\n",
    "  and can be downloaded using the tools in ``sklearn.datasets.load_*``\n",
    "- **Downloadable Data:** these larger datasets are available for download, and scikit-learn\n",
    "  includes tools which streamline this process.  These tools can be found in\n",
    "  ``sklearn.datasets.fetch_*``\n",
    "- **Generated Data:** there are several datasets which are generated from models based on a\n",
    "  random seed.  These are available in the ``sklearn.datasets.make_*``\n",
    "\n",
    "You can explore the available dataset loaders, fetchers, and generators using IPython's\n",
    "tab-completion functionality.  After importing the ``datasets`` submodule from ``sklearn``,\n",
    "type\n",
    "\n",
    "    datasets.load_ + TAB\n",
    "\n",
    "or\n",
    "\n",
    "    datasets.fetch_ + TAB\n",
    "\n",
    "or\n",
    "\n",
    "    datasets.make_ + TAB\n",
    "\n",
    "to see a list of available functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Type datasets.fetch_<TAB> or datasets.load_<TAB> in IPython to see all possibilities\n",
    "\n",
    "# datasets.fetch_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
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   "outputs": [],
   "source": [
    "# datasets.load_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the next section, we'll use some of these datasets and take a look at the basic principles of machine learning."
   ]
  }
 ],
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